Overview

Dataset statistics

Number of variables12
Number of observations145392
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.0 MiB
Average record size in memory130.1 B

Variable types

Numeric9
Categorical2
DateTime1

Alerts

demand_in_MW is highly overall correlated with hourHigh correlation
dow is highly overall correlated with weekdayHigh correlation
doy is highly overall correlated with month and 3 other fieldsHigh correlation
hour is highly overall correlated with demand_in_MWHigh correlation
month is highly overall correlated with doy and 3 other fieldsHigh correlation
quarter is highly overall correlated with doy and 3 other fieldsHigh correlation
season is highly overall correlated with doy and 3 other fieldsHigh correlation
weekday is highly overall correlated with dowHigh correlation
woy is highly overall correlated with doy and 3 other fieldsHigh correlation
dow has 20760 (14.3%) zerosZeros
hour has 6058 (4.2%) zerosZeros
weekday has 20760 (14.3%) zerosZeros

Reproduction

Analysis started2024-03-13 11:22:31.753327
Analysis finished2024-03-13 11:23:11.854974
Duration40.1 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

demand_in_MW
Real number (ℝ)

HIGH CORRELATION 

Distinct28455
Distinct (%)19.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32078.925
Minimum14544
Maximum62009
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-03-13T11:23:12.021850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum14544
5-th percentile22622
Q127571
median31420
Q335647
95-th percentile44187
Maximum62009
Range47465
Interquartile range (IQR)8076

Descriptive statistics

Standard deviation6464.2878
Coefficient of variation (CV)0.20151199
Kurtosis0.73653991
Mean32078.925
Median Absolute Deviation (MAD)4022
Skewness0.73903688
Sum4.664019 × 109
Variance41787017
MonotonicityNot monotonic
2024-03-13T11:23:12.310100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30051 24
 
< 0.1%
32719 22
 
< 0.1%
29798 22
 
< 0.1%
28095 21
 
< 0.1%
31116 21
 
< 0.1%
28157 21
 
< 0.1%
32731 21
 
< 0.1%
32244 21
 
< 0.1%
32973 20
 
< 0.1%
29189 20
 
< 0.1%
Other values (28445) 145179
99.9%
ValueCountFrequency (%)
14544 1
< 0.1%
14586 1
< 0.1%
14821 1
< 0.1%
14955 1
< 0.1%
15390 1
< 0.1%
15526 1
< 0.1%
15919 1
< 0.1%
16688 1
< 0.1%
17422 1
< 0.1%
17734 1
< 0.1%
ValueCountFrequency (%)
62009 1
< 0.1%
61909 1
< 0.1%
61796 1
< 0.1%
61770 1
< 0.1%
61646 1
< 0.1%
61643 1
< 0.1%
61641 1
< 0.1%
61610 1
< 0.1%
61608 1
< 0.1%
61532 1
< 0.1%

dow
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9995254
Minimum0
Maximum6
Zeros20760
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-03-13T11:23:12.566622image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9997128
Coefficient of variation (CV)0.66667639
Kurtosis-1.2496699
Mean2.9995254
Median Absolute Deviation (MAD)2
Skewness0.00053393314
Sum436107
Variance3.9988511
MonotonicityNot monotonic
2024-03-13T11:23:12.773495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 20784
14.3%
3 20784
14.3%
1 20783
14.3%
4 20761
14.3%
5 20760
14.3%
6 20760
14.3%
0 20760
14.3%
ValueCountFrequency (%)
0 20760
14.3%
1 20783
14.3%
2 20784
14.3%
3 20784
14.3%
4 20761
14.3%
5 20760
14.3%
6 20760
14.3%
ValueCountFrequency (%)
6 20760
14.3%
5 20760
14.3%
4 20761
14.3%
3 20784
14.3%
2 20784
14.3%
1 20783
14.3%
0 20760
14.3%

doy
Real number (ℝ)

HIGH CORRELATION 

Distinct366
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180.45525
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-03-13T11:23:13.034386image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile18
Q190
median179
Q3271
95-th percentile347
Maximum366
Range365
Interquartile range (IQR)181

Descriptive statistics

Standard deviation105.13873
Coefficient of variation (CV)0.5826305
Kurtosis-1.188963
Mean180.45525
Median Absolute Deviation (MAD)91
Skewness0.036789812
Sum26236750
Variance11054.153
MonotonicityNot monotonic
2024-03-13T11:23:13.441022image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
184 408
 
0.3%
135 408
 
0.3%
137 408
 
0.3%
138 408
 
0.3%
139 408
 
0.3%
140 408
 
0.3%
141 408
 
0.3%
142 408
 
0.3%
143 408
 
0.3%
144 408
 
0.3%
Other values (356) 141312
97.2%
ValueCountFrequency (%)
1 407
0.3%
2 408
0.3%
3 408
0.3%
4 408
0.3%
5 408
0.3%
6 408
0.3%
7 408
0.3%
8 408
0.3%
9 408
0.3%
10 408
0.3%
ValueCountFrequency (%)
366 96
 
0.1%
365 384
0.3%
364 384
0.3%
363 384
0.3%
362 384
0.3%
361 384
0.3%
360 384
0.3%
359 384
0.3%
358 384
0.3%
357 384
0.3%

year
Real number (ℝ)

Distinct17
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.8007
Minimum2002
Maximum2018
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-03-13T11:23:13.912824image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2002
5-th percentile2002
Q12006
median2010
Q32014
95-th percentile2017
Maximum2018
Range16
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.7917397
Coefficient of variation (CV)0.0023841865
Kurtosis-1.1966871
Mean2009.8007
Median Absolute Deviation (MAD)4
Skewness0.008915165
Sum2.9220894 × 108
Variance22.960769
MonotonicityIncreasing
2024-03-13T11:23:14.579785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2004 8784
 
6.0%
2008 8784
 
6.0%
2012 8784
 
6.0%
2016 8784
 
6.0%
2010 8760
 
6.0%
2017 8760
 
6.0%
2005 8760
 
6.0%
2006 8760
 
6.0%
2007 8760
 
6.0%
2009 8760
 
6.0%
Other values (7) 57696
39.7%
ValueCountFrequency (%)
2002 8759
6.0%
2003 8760
6.0%
2004 8784
6.0%
2005 8760
6.0%
2006 8760
6.0%
2007 8760
6.0%
2008 8784
6.0%
2009 8760
6.0%
2010 8760
6.0%
2011 8760
6.0%
ValueCountFrequency (%)
2018 5137
3.5%
2017 8760
6.0%
2016 8784
6.0%
2015 8760
6.0%
2014 8760
6.0%
2013 8760
6.0%
2012 8784
6.0%
2011 8760
6.0%
2010 8760
6.0%
2009 8760
6.0%

month
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4358355
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-03-13T11:23:14.946696image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4389666
Coefficient of variation (CV)0.53434657
Kurtosis-1.1976393
Mean6.4358355
Median Absolute Deviation (MAD)3
Skewness0.027390678
Sum935719
Variance11.826491
MonotonicityNot monotonic
2024-03-13T11:23:15.676038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 12648
8.7%
5 12648
8.7%
7 12648
8.7%
1 12647
8.7%
4 12240
8.4%
6 12240
8.4%
8 11953
8.2%
10 11904
8.2%
12 11904
8.2%
2 11520
7.9%
Other values (2) 23040
15.8%
ValueCountFrequency (%)
1 12647
8.7%
2 11520
7.9%
3 12648
8.7%
4 12240
8.4%
5 12648
8.7%
6 12240
8.4%
7 12648
8.7%
8 11953
8.2%
9 11520
7.9%
10 11904
8.2%
ValueCountFrequency (%)
12 11904
8.2%
11 11520
7.9%
10 11904
8.2%
9 11520
7.9%
8 11953
8.2%
7 12648
8.7%
6 12240
8.4%
5 12648
8.7%
4 12240
8.4%
3 12648
8.7%

quarter
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
2
37128 
1
36815 
3
36121 
4
35328 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145392
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 37128
25.5%
1 36815
25.3%
3 36121
24.8%
4 35328
24.3%

Length

2024-03-13T11:23:15.965896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T11:23:16.279113image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 37128
25.5%
1 36815
25.3%
3 36121
24.8%
4 35328
24.3%

Most occurring characters

ValueCountFrequency (%)
2 37128
25.5%
1 36815
25.3%
3 36121
24.8%
4 35328
24.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145392
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 37128
25.5%
1 36815
25.3%
3 36121
24.8%
4 35328
24.3%

Most occurring scripts

ValueCountFrequency (%)
Common 145392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 37128
25.5%
1 36815
25.3%
3 36121
24.8%
4 35328
24.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 37128
25.5%
1 36815
25.3%
3 36121
24.8%
4 35328
24.3%

hour
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros6058
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-03-13T11:23:16.535129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222104
Coefficient of variation (CV)0.60193134
Kurtosis-1.2041741
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum1672008
Variance47.916996
MonotonicityNot monotonic
2024-03-13T11:23:16.830648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 6058
 
4.2%
2 6058
 
4.2%
23 6058
 
4.2%
22 6058
 
4.2%
21 6058
 
4.2%
20 6058
 
4.2%
19 6058
 
4.2%
18 6058
 
4.2%
17 6058
 
4.2%
16 6058
 
4.2%
Other values (14) 84812
58.3%
ValueCountFrequency (%)
0 6058
4.2%
1 6058
4.2%
2 6058
4.2%
3 6058
4.2%
4 6058
4.2%
5 6058
4.2%
6 6058
4.2%
7 6058
4.2%
8 6058
4.2%
9 6058
4.2%
ValueCountFrequency (%)
23 6058
4.2%
22 6058
4.2%
21 6058
4.2%
20 6058
4.2%
19 6058
4.2%
18 6058
4.2%
17 6058
4.2%
16 6058
4.2%
15 6058
4.2%
14 6058
4.2%

weekday
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9995254
Minimum0
Maximum6
Zeros20760
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-03-13T11:23:17.093091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9997128
Coefficient of variation (CV)0.66667639
Kurtosis-1.2496699
Mean2.9995254
Median Absolute Deviation (MAD)2
Skewness0.00053393314
Sum436107
Variance3.9988511
MonotonicityNot monotonic
2024-03-13T11:23:17.347972image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 20784
14.3%
3 20784
14.3%
1 20783
14.3%
4 20761
14.3%
5 20760
14.3%
6 20760
14.3%
0 20760
14.3%
ValueCountFrequency (%)
0 20760
14.3%
1 20783
14.3%
2 20784
14.3%
3 20784
14.3%
4 20761
14.3%
5 20760
14.3%
6 20760
14.3%
ValueCountFrequency (%)
6 20760
14.3%
5 20760
14.3%
4 20761
14.3%
3 20784
14.3%
2 20784
14.3%
1 20783
14.3%
0 20760
14.3%

woy
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.217935
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 MiB
2024-03-13T11:23:17.672471image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q113
median26
Q339
95-th percentile50
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.019948
Coefficient of variation (CV)0.57288827
Kurtosis-1.1889584
Mean26.217935
Median Absolute Deviation (MAD)13
Skewness0.037334439
Sum3811878
Variance225.59882
MonotonicityNot monotonic
2024-03-13T11:23:17.988700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27 2856
 
2.0%
30 2856
 
2.0%
29 2856
 
2.0%
16 2856
 
2.0%
4 2856
 
2.0%
5 2856
 
2.0%
6 2856
 
2.0%
7 2856
 
2.0%
8 2856
 
2.0%
9 2856
 
2.0%
Other values (43) 116832
80.4%
ValueCountFrequency (%)
1 2831
1.9%
2 2856
2.0%
3 2856
2.0%
4 2856
2.0%
5 2856
2.0%
6 2856
2.0%
7 2856
2.0%
8 2856
2.0%
9 2856
2.0%
10 2856
2.0%
ValueCountFrequency (%)
53 504
 
0.3%
52 2688
1.8%
51 2688
1.8%
50 2688
1.8%
49 2688
1.8%
48 2688
1.8%
47 2688
1.8%
46 2688
1.8%
45 2688
1.8%
44 2688
1.8%

dom
Real number (ℝ)

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.722529
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.2 MiB
2024-03-13T11:23:18.283719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8013127
Coefficient of variation (CV)0.55978987
Kurtosis-1.1940985
Mean15.722529
Median Absolute Deviation (MAD)8
Skewness0.0071063028
Sum2285930
Variance77.463105
MonotonicityNot monotonic
2024-03-13T11:23:18.538903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
2 4800
 
3.3%
1 4799
 
3.3%
3 4777
 
3.3%
28 4776
 
3.3%
27 4776
 
3.3%
26 4776
 
3.3%
25 4776
 
3.3%
24 4776
 
3.3%
23 4776
 
3.3%
22 4776
 
3.3%
Other values (21) 97584
67.1%
ValueCountFrequency (%)
1 4799
3.3%
2 4800
3.3%
3 4777
3.3%
4 4776
3.3%
5 4776
3.3%
6 4776
3.3%
7 4776
3.3%
8 4776
3.3%
9 4776
3.3%
10 4776
3.3%
ValueCountFrequency (%)
31 2784
1.9%
30 4368
3.0%
29 4464
3.1%
28 4776
3.3%
27 4776
3.3%
26 4776
3.3%
25 4776
3.3%
24 4776
3.3%
23 4776
3.3%
22 4776
3.3%

date
Date

Distinct6059
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
Minimum2002-01-01 00:00:00
Maximum2018-08-03 00:00:00
2024-03-13T11:23:18.826545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:19.124233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

season
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.2 MiB
2
37536 
3
36841 
1
36071 
4
34944 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters145392
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
2 37536
25.8%
3 36841
25.3%
1 36071
24.8%
4 34944
24.0%

Length

2024-03-13T11:23:19.427035image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T11:23:19.786822image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2 37536
25.8%
3 36841
25.3%
1 36071
24.8%
4 34944
24.0%

Most occurring characters

ValueCountFrequency (%)
2 37536
25.8%
3 36841
25.3%
1 36071
24.8%
4 34944
24.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145392
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 37536
25.8%
3 36841
25.3%
1 36071
24.8%
4 34944
24.0%

Most occurring scripts

ValueCountFrequency (%)
Common 145392
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 37536
25.8%
3 36841
25.3%
1 36071
24.8%
4 34944
24.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 145392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 37536
25.8%
3 36841
25.3%
1 36071
24.8%
4 34944
24.0%

Interactions

2024-03-13T11:23:08.224602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:46.980582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:50.095257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:52.573907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:55.148579image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:57.590082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:59.923868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:02.940444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:05.873059image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:08.470341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:47.236795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:50.463419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:52.839283image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:55.410832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:57.831537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:00.176517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:03.251242image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:06.129358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:08.726549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:47.516997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:50.751212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:53.088851image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:55.683615image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:58.097420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:00.472832image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:03.746557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:06.405075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:08.986074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:47.854146image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:51.017658image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:53.359030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:55.960857image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:58.364891image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:00.854545image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:04.025024image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:06.675756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:09.254144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:48.274075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:51.295056image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:53.842898image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:56.241528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:58.640873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:01.191074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:04.311818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:06.948899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:09.506375image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:48.629268image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:51.550908image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:54.089107image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:56.504807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:58.879582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:01.527614image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:04.687229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:07.196533image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:09.745460image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:48.981502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:51.797525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:54.345778image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:56.758115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:59.134499image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:01.870675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:04.924613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:07.463330image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:10.009855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:49.331511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:52.055954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:54.618131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:57.046369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:59.396364image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:02.278437image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:05.331820image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:07.729094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:10.252457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:49.759280image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:52.315911image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:54.881079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:57.311188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:22:59.667097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:02.571359image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:05.600494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-03-13T11:23:07.977108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-03-13T11:23:20.141061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
demand_in_MWdomdowdoyhourmonthquarterseasonweekdaywoyyear
demand_in_MW1.000-0.001-0.194-0.0640.514-0.0640.2490.308-0.194-0.065-0.068
dom-0.0011.000-0.0010.094-0.0000.0100.0140.014-0.0010.068-0.001
dow-0.194-0.0011.000-0.000-0.000-0.0000.0000.0001.000-0.001-0.000
doy-0.0640.094-0.0001.000-0.0000.9970.9250.903-0.0000.973-0.042
hour0.514-0.000-0.000-0.0001.000-0.0000.0000.000-0.000-0.000-0.000
month-0.0640.010-0.0000.997-0.0001.0001.0000.943-0.0000.971-0.042
quarter0.2490.0140.0000.9250.0001.0001.0000.639-0.0000.947-0.041
season0.3080.0140.0000.9030.0000.9430.6391.0000.0010.588-0.026
weekday-0.194-0.0011.000-0.000-0.000-0.000-0.0000.0011.000-0.001-0.000
woy-0.0650.068-0.0010.973-0.0000.9710.9470.588-0.0011.000-0.041
year-0.068-0.001-0.000-0.042-0.000-0.042-0.041-0.026-0.000-0.0411.000

Missing values

2024-03-13T11:23:10.677130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T11:23:11.246108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

demand_in_MWdowdoyyearmonthquarterhourweekdaywoydomdateseason
2002-01-01 01:00:0030393.01120021111112002-01-011
2002-01-01 02:00:0029265.01120021121112002-01-011
2002-01-01 03:00:0028357.01120021131112002-01-011
2002-01-01 04:00:0027899.01120021141112002-01-011
2002-01-01 05:00:0028057.01120021151112002-01-011
2002-01-01 06:00:0028654.01120021161112002-01-011
2002-01-01 07:00:0029308.01120021171112002-01-011
2002-01-01 08:00:0029595.01120021181112002-01-011
2002-01-01 09:00:0029943.01120021191112002-01-011
2002-01-01 10:00:0030692.011200211101112002-01-011
demand_in_MWdowdoyyearmonthquarterhourweekdaywoydomdateseason
2018-08-02 15:00:0047154.032142018831533122018-08-023
2018-08-02 16:00:0046989.032142018831633122018-08-023
2018-08-02 17:00:0046816.032142018831733122018-08-023
2018-08-02 18:00:0046760.032142018831833122018-08-023
2018-08-02 19:00:0045641.032142018831933122018-08-023
2018-08-02 20:00:0044057.032142018832033122018-08-023
2018-08-02 21:00:0043256.032142018832133122018-08-023
2018-08-02 22:00:0041552.032142018832233122018-08-023
2018-08-02 23:00:0038500.032142018832333122018-08-023
2018-08-03 00:00:0035486.04215201883043132018-08-033